发现交互系统关系的潜在表示(CS)

实体之间相互作用的系统是通用的。在许多相互作用的系统中，很难观察到实体之间的关系，而实体之间的关系是分析系统的关键信息。近年来，人们对利用图神经网络发现实体之间的关系越来越感兴趣。但是，如果关系数量未知或关系复杂，现有的方法难以应用。我们提出了发现潜在关系(discovery Latent Relation, DSLR)模型，该模型在关系数量未知或存在多种关系的情况下也能灵活应用。DSLR模型的灵活性来自于编码器的设计理念，编码器表示潜在空间中实体之间的关系，而不是离散变量，解码器可以处理多种类型的关系。我们对具有实体间各种关系的合成图数据和真实图数据进行了实验，并将定性和定量结果与其他方法进行了比较。实验表明，该方法适用于复杂关系数目未知的动态图的分析。

原文题目：Discovering Latent Representations of Relations for Interacting Systems

原文：Systems whose entities interact with each other are common. In many interacting systems, it is difficult to observe the relations between entities which is the key information for analyzing the system. In recent years, there has been increasing interest in discovering the relationships between entities using graph neural networks. However, existing approaches are difficult to apply if the number of relations is unknown or if the relations are complex. We propose the DiScovering Latent Relation (DSLR) model, which is flexibly applicable even if the number of relations is unknown or many types of relations exist. The flexibility of our DSLR model comes from the design concept of our encoder that represents the relation between entities in a latent space rather than a discrete variable and a decoder that can handle many types of relations. We performed the experiments on synthetic and real-world graph data with various relationships between entities, and compared the qualitative and quantitative results with other approaches. The experiments show that the proposed method is suitable for analyzing dynamic graphs with an unknown number of complex relations.